
"""simple docstring"""


import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
      import jax
      import jax.numpy as jnp



@slow
@require_flax
class 	__a	(   unittest.TestCase						):






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				List[str]      )->	Tuple:







              """simple docstring"""
              return f"gaussian_noise_s={seed}_shape={'_'.join([str(UpperCAmelCase_      ) for s in shape]      )}.npy"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	Tuple:







              """simple docstring"""
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				Any=0							,   UpperCAmelCase_			:				Any=(4, 4, 64, 64)							,   UpperCAmelCase_			:				Tuple=False      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		jnp.bfloataa if fpaa else jnp.floataa
              UpperCamelCase       			=		jnp.array(load_hf_numpy(self.get_file_format(UpperCAmelCase_							,   UpperCAmelCase_      )      )							,   dtype=UpperCAmelCase_      )
              return image






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str							,   UpperCAmelCase_			:				Any=False							,   UpperCAmelCase_			:				str="CompVis/stable-diffusion-v1-4"      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		jnp.bfloataa if fpaa else jnp.floataa
              UpperCamelCase       			=		"bf16" if fpaa else None

              UpperCamelCase			,							UpperCamelCase       			=		FlaxUNetaDConditionModel.from_pretrained(
                  UpperCAmelCase_							,   subfolder="unet"							,   dtype=UpperCAmelCase_							,   revision=UpperCAmelCase_      )
              return model, params






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str							,   UpperCAmelCase_			:				Union[str, Any]=0							,   UpperCAmelCase_			:				Optional[int]=(4, 77, 768)							,   UpperCAmelCase_			:				Tuple=False      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		jnp.bfloataa if fpaa else jnp.floataa
              UpperCamelCase       			=		jnp.array(load_hf_numpy(self.get_file_format(UpperCAmelCase_							,   UpperCAmelCase_      )      )							,   dtype=UpperCAmelCase_      )
              return hidden_states






       @parameterized.expand(
           [
               # fmt: off
               [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
               [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
               [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
               [3, 1_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
               # fmt: on
           ]      )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				Any      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase       			=		self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"							,   fpaa=UpperCAmelCase_      )
              UpperCamelCase       			=		self.get_latents(UpperCAmelCase_							,   fpaa=UpperCAmelCase_      )
              UpperCamelCase       			=		self.get_encoder_hidden_states(UpperCAmelCase_							,   fpaa=UpperCAmelCase_      )

              UpperCamelCase       			=		model.apply(
                  {"params": params}							,   UpperCAmelCase_							,   jnp.array(UpperCAmelCase_							,   dtype=jnp.intaa      )							,   encoder_hidden_states=UpperCAmelCase_							,   ).sample

              assert sample.shape == latents.shape

              UpperCamelCase       			=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())      )							,   dtype=jnp.floataa      )
              UpperCamelCase       			=		jnp.array(UpperCAmelCase_							,   dtype=jnp.floataa      )

              # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
              assert jnp.allclose(UpperCAmelCase_							,   UpperCAmelCase_							,   atol=1e-2      )






       @parameterized.expand(
           [
               # fmt: off
               [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
               [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
               [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
               [3, 1_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
               # fmt: on
           ]      )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				str							,   UpperCAmelCase_			:				Optional[Any]							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				int      )->	Dict:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase       			=		self.get_unet_model(model_id="stabilityai/stable-diffusion-2"							,   fpaa=UpperCAmelCase_      )
              UpperCamelCase       			=		self.get_latents(UpperCAmelCase_							,   shape=(4, 4, 96, 96)							,   fpaa=UpperCAmelCase_      )
              UpperCamelCase       			=		self.get_encoder_hidden_states(UpperCAmelCase_							,   shape=(4, 77, 1_024)							,   fpaa=UpperCAmelCase_      )

              UpperCamelCase       			=		model.apply(
                  {"params": params}							,   UpperCAmelCase_							,   jnp.array(UpperCAmelCase_							,   dtype=jnp.intaa      )							,   encoder_hidden_states=UpperCAmelCase_							,   ).sample

              assert sample.shape == latents.shape

              UpperCamelCase       			=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())      )							,   dtype=jnp.floataa      )
              UpperCamelCase       			=		jnp.array(UpperCAmelCase_							,   dtype=jnp.floataa      )

              # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
              assert jnp.allclose(UpperCAmelCase_							,   UpperCAmelCase_							,   atol=1e-2      )







"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor



class 	__a	(   unittest.TestCase						):






       def __init__(	self			:				Optional[Any]							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				Optional[Any]=7							,   UpperCAmelCase_			:				List[Any]=3							,   UpperCAmelCase_			:				Optional[int]=30							,   UpperCAmelCase_			:				Any=400							,   UpperCAmelCase_			:				Any=True							,   UpperCAmelCase_			:				Dict=None							,   UpperCAmelCase_			:				Dict=0.9							,   UpperCAmelCase_			:				Tuple=None							,   UpperCAmelCase_			:				Tuple=True							,   UpperCAmelCase_			:				Optional[int]=[0.5, 0.5, 0.5]							,   UpperCAmelCase_			:				Union[str, Any]=[0.5, 0.5, 0.5]							,   )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		size if size is not None else {"shortest_edge": 30}
              UpperCamelCase       			=		crop_size if crop_size is not None else {"height": 30, "width": 30}
              UpperCamelCase       			=		parent
              UpperCamelCase       			=		batch_size
              UpperCamelCase       			=		num_channels
              UpperCamelCase       			=		min_resolution
              UpperCamelCase       			=		max_resolution
              UpperCamelCase       			=		do_resize_and_center_crop
              UpperCamelCase       			=		size
              UpperCamelCase       			=		crop_pct
              UpperCamelCase       			=		crop_size
              UpperCamelCase       			=		do_normalize
              UpperCamelCase       			=		image_mean
              UpperCamelCase       			=		image_std






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	str:







              """simple docstring"""
              return {
                  "size": self.size,
                  "do_resize_and_center_crop": self.do_resize_and_center_crop,
                  "crop_pct": self.crop_pct,
                  "crop_size": self.crop_size,
                  "do_normalize": self.do_normalize,
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
              }





@require_torch
@require_vision
class 	__a	(   _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       List[Any]				=      PoolFormerImageProcessor if is_vision_available() else None






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		PoolFormerImageProcessingTester(self      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Optional[Any]:







              """simple docstring"""
              return self.image_processor_tester.prepare_image_processor_dict()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "do_resize_and_center_crop"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "size"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "crop_pct"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "do_normalize"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "image_mean"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "image_std"      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.image_processing_class.from_dict(self.image_processor_dict      )
              self.assertEqual(image_processor.size							,   {"shortest_edge": 30}      )
              self.assertEqual(image_processor.crop_size							,   {"height": 30, "width": 30}      )

              UpperCamelCase       			=		self.image_processing_class.from_dict(self.image_processor_dict							,   size=42							,   crop_size=84      )
              self.assertEqual(image_processor.size							,   {"shortest_edge": 42}      )
              self.assertEqual(image_processor.crop_size							,   {"height": 84, "width": 84}      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Any:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Dict:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random PIL images
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   Image.Image      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values

              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[str]:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random numpy tensors
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_							,   numpify=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   np.ndarray      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	str:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random PyTorch tensors
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_							,   torchify=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   torch.Tensor      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )





